Prediction of molecular markers of bovine mastitis by meta-analysis of differentially expressed genes using combined p-value and robust rank aggregation

Bovine mastitis causes significant economic loss to the dairy industry by affecting milk quality and quantity. E.coli and S.aureus are the two common mastitis-causing bacteria among the consortia of mastitis pathogens, wherein E.coli is an opportunistic environmental pathogen, and S.aureus is a contagious pathogen. This study was designed to predict molecular markers of bovine mastitis by meta-analysis of differentially expressed genes (DEG) in E.coli or S.aureus infected mammary epithelial cells (MECs) using p-value combination and robust rank aggregation (RRA) methods. High throughput transcriptome of bovine (MECs, infected with E.coli or S.aureus, were analyzed, and correlation of z-scores were computed for the expression datasets to identify the lineage profile and functional ontology of DEGs. Key pathways enriched in infected MECs were deciphered by Gene Set Enrichment Analysis (GSEA), following which combined p-value and RRA were used to perform DEG meta-analysis to limit type I error in the analysis. The miRNA-Gene networks were then built to uncover potential molecular markers of mastitis. Lineage profiling of MECs showed that the gene expression levels were associated with mammary tissue lineage. The up-regulated genes were enriched in immune-related pathways whereas down-regulated genes influenced the cellular processes. GSEA analysis of DEGs deciphered the involvement of Toll-like receptor (TLR), and NF- Kappa B signalling pathway during infection. Comparison after meta-analysis yielded with genes ZC3H12A, RND1 and MAP3K8 having significant expression levels in both E.coli and S.aureus dataset and on evaluating miRNA-Gene network 7 pairs were common to both sets identifying them as potential molecular markers.

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Transcriptomic profiles in Parkinson’s disease

Experimental Biology and Medicine ◽

10.1177/1535370220967325 ◽

2020 ◽

pp. 153537022096732

Author(s):

Lille Kurvits ◽

Freddy Lättekivi ◽

Ene Reimann ◽

Liis Kadastik-Eerme ◽

Kristjan M Kasterpalu ◽

...

Keyword(s):

Gene Expression ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Substantia Nigra ◽

Brain Tissue ◽

Differentially Expressed Genes ◽

Meta Analysis ◽

Rank Aggregation ◽

Differentially Expressed ◽

Peripheral Tissues

Transcriptomics in Parkinson’s disease offers insights into the pathogenesis of Parkinson’s disease but obtaining brain tissue has limitations. In order to bypass this issue, we profile and compare differentially expressed genes and enriched pathways (KEGG) in two peripheral tissues (blood and skin) of 12 Parkinson’s disease patients and 12 healthy controls using RNA-sequencing technique and validation with RT-qPCR. Furthermore, we compare our results to previous Parkinson’s disease post mortem brain tissue and blood results using the robust rank aggregation method. The results show no overlapping differentially expressed genes or enriched pathways in blood vs. skin in our sample sets (25 vs. 1068 differentially expressed genes with an FDR ≤ 0.05; 1 vs. 9 pathways in blood and skin, respectively). A meta-analysis from previous transcriptomic sample sets using either microarrays or RNA-Seq yields a robust rank aggregation list of cortical gene expression changes with 43 differentially expressed genes; a list of substantia nigra changes with 2 differentially expressed genes and a list of blood changes with 1 differentially expressed gene being statistically significant at FDR ≤ 0.05. In cortex 1, KEGG pathway was enriched, four in substantia nigra and two in blood. None of the differentially expressed genes or pathways overlap between these tissues. When comparing our previously published skin transcription analysis, two differentially expressed genes between the cortex robust rank aggregation and skin overlap. In this study, for the first time a meta-analysis is applied on transcriptomic sample sets in Parkinson’s disease. Simultaneously, it explores the notion that Parkinson’s disease is not just a neuronal tissue disease by exploring peripheral tissues. The comparison of different Parkinson’s disease tissues yields surprisingly few significant differentially expressed genes and pathways, suggesting that divergent gene expression profiles in distinct cell lineages, metabolic and possibly iatrogenic effects create too much transcriptomic noise for detecting significant signal. On the other hand, there are signs that point towards Parkinson’s disease-specific changes in non-neuronal peripheral tissues in Parkinson’s disease, indicating that Parkinson’s disease might be a multisystem disorder.

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Source of Dietary Fat in Pig Diet Affects Adipose Expression of Genes Related to Cancer, Cardiovascular, and Neurodegenerative Diseases

Genes ◽

10.3390/genes10120948 ◽

2019 ◽

Vol 10 (12) ◽

pp. 948

Author(s):

Maria Oczkowicz ◽

Tomasz Szmatoła ◽

Małgorzata Świątkiewicz

Keyword(s):

Differentially Expressed Genes ◽

Beef Tallow ◽

Rapeseed Oil ◽

Fluid Shear Stress ◽

Coconut Oil ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Differentially Expressed ◽

Coagulation Cascade ◽

P Value

It has been known for many years that excessive consumption of saturated fats has proatherogenic properties, contrary to unsaturated fats. However, the molecular mechanism covering these effects is not fully understood. In this paper, we aimed to identify differentially expressed genes (DEGs) using RNA-sequencing, following feeding pigs with different sources of fat. After comparison of adipose samples from three dietary groups (rapeseed oil (n = 6), beef tallow (n = 5), coconut oil (n = 5)), we identified 29 DEGs (adjusted p-value < 0.05, fold change > 1.3) between beef tallow and rapeseed oil and 2 genes between coconut oil and rapeseed oil groups. No differentially expressed genes were observed between coconut oil and beef tallow groups. Almost all 29 DEGs between rapeseed oil and beef tallow groups are connected to neurodegenerative, cardiovascular diseases, or cancer (e.g., PLAU, CYBB, NCF2, ZNF217, CHAC1, CTCFL). Functional analysis of these genes revealed that they are associated with fluid shear stress response, complement and coagulation cascade, ROS signaling, neurogenesis, and regulation of protein binding and protein catabolic processes. Furthermore, gene set enrichment analysis (GSEA) of the whole datasets from all three comparisons suggests that both beef tallow and coconut oil may trigger changes in the expression level of genes crucial in the pathogenesis of civilization diseases.

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Microarray Gene Expression Dataset Re-Analysis Reveals Variability in Influenza Infection and Vaccination

10.1101/702068 ◽

2019 ◽

Author(s):

Lavida R. K. Rogers ◽

Gustavo de los Campos ◽

George I. Mias

Keyword(s):

Influenza Vaccination ◽

Differentially Expressed Genes ◽

Influenza Infection ◽

Meta Analysis ◽

Differentially Expressed ◽

P Value ◽

Expression Data ◽

Microarray Expression Data ◽

Microarray Expression ◽

Age And Sex

ABSTRACTInfluenza, a communicable disease, affects thousands of people worldwide. Young children, elderly, immunocompromised individuals and pregnant women are at higher risk for being infected by the influenza virus. Our study aims to highlight differentially expressed genes in influenza disease compared to influenza vaccination. We also investigate genetic variation due to the age and sex of samples. To accomplish our goals, we conducted a meta-analysis using publicly available microarray expression data. Our inclusion criteria included subjects with influenza, subjects who received the influenza vaccine and healthy controls. We curated 18 microarray datasets for a total of 3,481 samples (1,277 controls, 297 influenza infection, 1,907 influenza vaccination). We pre-processed the raw microarray expression data in R using packages available to pre-process Affymetrix and Illumina microarray platforms. We used a Box-Cox power transformation of the data prior to our down-stream analysis to identify differentially expressed genes. Statistical analyses were based on linear mixed effects model with all study factors and successive likelihood ratio tests (LRT) to identify differentially-expressed genes. We filtered LRT results by disease (Bonferroni adjusted p-value < 0.05) and used a two-tailed 10% quantile cutoff to identify biologically significant genes. Furthermore, we assessed age and sex effects on the disease genes by filtering for genes with a statistically significant (Bonferroni adjusted p-value < 0.05) interaction between disease and age, and disease and sex. We identified 4,889 statistically significant genes when we filtered the LRT results by disease factor, and gene enrichment analysis (gene ontology and pathways) included innate immune response, viral process, defense response to virus, Hematopoietic cell lineage and NF-kappa B signaling pathway. Our quantile filtered gene lists comprised of 978 genes each associated with influenza infection and vaccination. We also identified 907 and 48 genes with statistically significant (Bonferroni adjusted p-value < 0.05) disease-age and disease-sex interactions respectively. Our meta-analysis approach highlights key gene signatures and their associated pathways for both influenza infection and vaccination. We also were able to identify genes with an age and sex effect. This gives potential for improving current vaccines and exploring genes that are expressed equally across ages when considering universal vaccinations for influenza.

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C3aR1 Correlates With Immune Cell Infiltration and Serves as Prognostic and Therapeutic Biomarker in Gastric Cancer

10.21203/rs.3.rs-1163983/v1 ◽

2021 ◽

Author(s):

weifeng liu ◽

Zhijie Chu ◽

Cheng Yang ◽

Tianbao Yang ◽

Yanhui Yang ◽

...

Keyword(s):

Gastric Cancer ◽

T Cell ◽

Differentially Expressed Genes ◽

Tumor Tissue ◽

Immune Cell ◽

Gene Set Enrichment Analysis ◽

Differentially Expressed ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Expression Levels

Abstract As the fourth most common malignancy worldwide, gastric cancer can lead more than 720 000 patient death every year. Precisely therapeutic intervention can significantly improve patients’ survival status underlying the precise clarification by molecular indexes. Identifying the biomarkers highly associated with disease prognosis will be helpful to guide the clinical therapy. C3ar1 is an essential receptor in the complement system, and participates in various biological processes associated with immunological responses. To identify the crucial roles of C3AR1 in gastric cancer tmorigenesis, we determined the mRNA profile, protein expression levels and the clinicopathological indexes using cBioportal, Kaplan-Meier plotter and the Human Protein Atlas databases. To identify the molecular network in C3AR1-expressed gastric cancer, we obtained the differentially expressed genes using the GEPIA database compared with normal stomach tissues. Furthermore, we analyzed the biological impact of these differentially expressed genes using protein-protein interaction network and gene set enrichment analysis, in which we identified the hub genes and critical pathways influenced by over-expressed C3AR1 in gastric cancer. Finally, we evaluated the correlation between the C3AR1 expression levels and immune cell infiltration levels utilizing the Tumor Immunoassay Resource database. Our results revealed that the higher expression level of C3AR1 can lead higher infiltration of T cell CD8+, T cell CD4+, macrophage, neutrophil, B cell and myeloid dendritic cells into tumor tissue. Moreover, we also found that higher infiltration of macrophage cells into tumor tissue can worsen the survival of patients with gastric cancer, which may be highly associated with the polarization states of macrophages (TAM and M2 status). Our investigation suggest that C3AR1 can be as an efficient diagnostic biomarkers for gastric cancer therapy.

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Meta-analysis of Transcriptomic Data Reveals Pathophysiological Modules Involved with Atrial Fibrillation

10.1101/2020.05.13.20100867 ◽

2020 ◽

Author(s):

Rodrigo Haas Bueno ◽

Mariana Recamonde-Mendoza

Keyword(s):

Atrial Fibrillation ◽

Differentially Expressed Genes ◽

Complex Disease ◽

Meta Analysis ◽

Differential Expression Analysis ◽

Interaction Network ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Differentially Expressed ◽

P Value

Atrial fibrillation (AF) is a complex disease and affects millions of people around the world. The biological mechanisms that are involved with AF are complex and still need to be fully elucidated. Therefore, we performed a meta-analysis of transcriptome data related to AF to explore these mechanisms aiming at more sensitive and reliable results. Public transcriptomic datasets were downloaded, analyzed for quality control, and individually pre-processed. Differential expression analysis was carried out for each individual dataset, and the results were meta-analytically aggregated using the r-th ordered p-value method. We analyzed the final list of differentially expressed genes through network analysis, namely topological and modularity analysis, and functional enrichment analysis. The meta-analysis of transcriptomes resulted in 589 differentially expressed genes, whose protein-protein interaction network presented 11 hubs-bottlenecks and four main identified functional modules. These modules were enriched for, respectively, 23, 54, 33, and 53 biological pathways involved with the pathophysiology of AF, especially with the disease's structural and electrical remodeling processes. Stress of the endoplasmic reticulum, protein catabolism, oxidative stress, and inflammation are some of the enriched processes. Among hubs-bottlenecks genes, which are highly connected and probably have a key role in regulating these processes, we found HSPA5, ANK2, CTNNB1, and VWF. Further experimental investigation of our findings may shed light on the pathophysiology of the disease and contribute to the identification of new therapeutic targets and treatments.

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Gene Expression and Epigenetic Analysis in Relapsed/Refractory Diffuse Large B Cell Lymphoma Provides Insights into Evolution of Treatment Resistance to R-CHOP

Blood ◽

10.1182/blood-2020-138645 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 26-26

Author(s):

Manishkumar S. Patel ◽

Ellen K. Kendall ◽

Sarah Ondrejka ◽

Agrima Mian ◽

Yazeed Sawalha ◽

...

Keyword(s):

Gene Expression ◽

B Cell ◽

Differentially Expressed Genes ◽

Research Funding ◽

B Cell Lymphoma ◽

Cell Receptor ◽

B Cell Receptor ◽

Gene Set Enrichment Analysis ◽

Differentially Expressed ◽

P Value

Background Diffuse large B cell lymphoma (DLBCL) is curable in ~60-70% of patients using standard chemoimmunotherapy, but the prognosis is poor for relapsed/refractory (R/R) DLBCL. Therefore, understanding the underlying molecular mechanisms will facilitate early prediction and effective management of resistance to therapy. Recent studies of paired diagnostic-relapse biopsies from patients have relied on a single "omics" approach, examining either gene expression or epigenetic evolution. Here we present a combined analysis of gene expression and DNA methylation profiles of paired diagnostic-relapse DLBCL biopsies to identify changes responsible for relapse after R-CHOP. Methods Biopsies from 23 DLBCL patients were obtained at the time of diagnosis and relapse following frontline R-CHOP chemoimmunotherapy. The cohort had 18 (78.3%) male patients with median age of 62 (range, 35-86) years and median IPI of 2.5 (range, 1-5). The median time from diagnosis to relapse was 7 (range, 0-57) months. DNA and RNA were extracted simultaneously from formalin-fixed paraffin embedded (FFPE) biopsy samples. DNA methylation levels were measured through Illumina 850k Methylation Array for 22 pairs of diagnostic-relapse biopsies. RNA from diagnostic-relapse paired biopsies from 6 patients was sequenced using Illumina HiSeq4000. Differentially methylated probes were identified using the DMRcate package, and differentially expressed genes were identified using the DESeq2 package. Gene set enrichment analysis was performed using canonical pathway gene sets from MSigDB. Pearson's correlation with a Bonferroni correction to the p-value was used to calculate the correlation between regularized log transformed gene expression counts and methylation beta values. Results In a pairwise comparison of gene expression between diagnostic and R/R biopsy pairs, we found 14 differentially expressed genes (FDR<0.1 & Log2FC>|1|) consistent across all pairs. Compared to gene expression at diagnosis, five genes (CYP1B1, LGR4, ATXN1, CTSC, ZMAT3) were downregulated, and eight genes (ERBB3, CD19, CARD11, MT-RNR2, IGHG3, CCDC88C, ATP2A3, CENPE, and PCNT) were up-regulated in the R/R samples. Many of these genes have been previously implicated in oncogenesis, such as ERBB3, a member of the epidermal growth receptor family. Importantly, some of these genes have known roles in DLBCL biology, such as CD19, a member of the B-cell receptor complex, and CARD11, a gene in which several oncogenic mutations have been identified in DLBCL as a mediator of NF-KB activation. Gene set enrichment analysis revealed overexpression of immune signatures such as cytokine-cytokine receptor interaction, chemokine receptor-chemokine binding, and the IL-12-STAT4 pathway at diagnosis. At relapse, cell cycle, B-cell receptor, and NOTCH signaling pathways were overexpressed. Interestingly, in a pairwise comparison of methylation between diagnostic and R/R biopsy pairs, there were no differentially methylated probes (FDR<0.05), suggesting no coordinated epigenetic evolution between diagnostic and R/R pairs. For biopsy pairs that had both gene expression and methylation data (5 pairs), we correlated gene expression and methylation values. We found that none of the differentially expressed genes between the diagnostic and R/R biopsies were significantly correlated with methylation status (adjusted p-value<0.05). Conclusions By analyzing paired diagnostic and relapse DLBCL biopsies, we found that at the time of relapse, there are significant transcriptomic changes but no significant epigenetic changes when compared to diagnostic biopsies. Activation of B-cell receptor and NOTCH signaling, as well as the loss of immune signaling at relapse, cannot be attributed to coordinated epigenetic changes in methylation. As the epigenetic profile of the biopsies did not consistently evolve, these data emphasize the need for better understanding of the baseline methylation profiles at the time of diagnosis, as well as acquired somatic mutations that may contribute to the emergence of therapeutic resistance. Future studies are needed to focus on how activation of signaling pathways triggered by genomic alterations can be targeted in relapsed/refractory DLBCL. Disclosures Hsi: Seattle Genetics: Consultancy, Honoraria; Miltenyi: Consultancy, Honoraria; Abbvie: Research Funding; Eli Lilly: Research Funding; CytomX: Consultancy, Honoraria. Hill:Takeda: Research Funding; Genentech: Consultancy, Honoraria, Research Funding; Karyopharm: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria, Research Funding; Pharmacyclics: Consultancy, Honoraria, Research Funding; Beigene: Consultancy, Honoraria, Research Funding; AstraZenica: Consultancy, Honoraria, Research Funding; Kite, a Gilead Company: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria; BMS: Consultancy, Honoraria, Research Funding.

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Meta-analysis based gene expression profiling reveals functional genes in ovarian cancer

Bioscience Reports ◽

10.1042/bsr20202911 ◽

2020 ◽

Vol 40 (11) ◽

Author(s):

Lin Zhao ◽

Yuhui Li ◽

Zhen Zhang ◽

Jing Zou ◽

Jianfu Li ◽

...

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Gene Expression Profiling ◽

Differentially Expressed Genes ◽

Expression Profiling ◽

Molecular Mechanisms ◽

Meta Analysis ◽

Differentially Expressed ◽

Functional Genes ◽

P Value

Abstract Background: Ovarian cancer causes high mortality rate worldwide, and despite numerous attempts, the outcome for patients with ovarian cancer are still not well improved. Microarray-based gene expressional analysis provides with valuable information for discriminating functional genes in ovarian cancer development and progression. However, due to the differences in experimental design, the results varied significantly across individual datasets. Methods: In the present study, the data of gene expression in ovarian cancer were downloaded from Gene Expression Omnibus (GEO) and 16 studies were included. A meta-analysis based gene expression analysis was performed to identify differentially expressed genes (DEGs). The most differentially expressed genes in our meta-analysis were selected for gene expression and gene function validation. Results: A total of 972 DEGs with P-value < 0.001 were identified in ovarian cancer, including 541 up-regulated genes and 431 down-regulated genes, among which 92 additional DEGs were found as gained DEGs. Top five up- and down-regulated genes were selected for the validation of gene expression profiling. Among these genes, up-regulated CD24 molecule (CD24), SRY (sex determining region Y)-box transcription factor 17 (SOX17), WFDC2, epithelial cell adhesion molecule (EPCAM), innate immunity activator (INAVA), and down-regulated aldehyde oxidase 1 (AOX1) were revealed to be with consistent expressional patterns in clinical patient samples of ovarian cancer. Gene functional analysis demonstrated that up-regulated WFDC2 and INAVA promoted ovarian cancer cell migration, WFDC2 enhanced cell proliferation, while down-regulated AOX1 was functional in inducing cell apoptosis of ovarian cancer. Conclusion: Our study shed light on the molecular mechanisms underlying the development of ovarian cancer, and facilitated the understanding of novel diagnostic and therapeutic targets in ovarian cancer.

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Meta-analysis of Gene Expression Microarray Datasets in Chronic Obstructive Pulmonary Disease

10.1101/671206 ◽

2019 ◽

Author(s):

Lavida R. K. Rogers ◽

Madison Verlinde ◽

George I. Mias

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Smoking Status ◽

Meta Analysis ◽

Differentially Expressed ◽

Chronic Obstructive ◽

P Value ◽

Obstructive Pulmonary Disease ◽

Microarray Datasets ◽

Microarray Expression

AbstractChronic obstructive pulmonary disease (COPD) was classified by the Centers for Disease Control and Prevention in 2014 as the 3rd leading cause of death in the United States (US). The main cause of COPD is exposure to tobacco smoke and air pollutants. Problems associated with COPD include under-diagnosis of the disease and an increase in the number of smokers worldwide. The goal of our study is to identify disease variability in the gene expression profiles of COPD subjects compared to controls. We used pre-existing, publicly available microarray expression datasets to conduct a meta-analysis. Our inclusion criteria for microarray datasets selected for smoking status, age and sex of blood donors reported. Our datasets used Affymetrix, Agilent microarray platforms (7 datasets, 1,262 samples). We re-analyzed the curated raw microarray expression data using R packages, and used Box-Cox power transformations to normalize datasets. To identify significant differentially expressed genes we ran an analysis of variance with a linear model with disease state, age, sex, smoking status and study as effects that also included binary interactions. We found 1,513 statistically significant (Benjamini-Hochberg-adjusted p-value <0.05) differentially expressed genes with respect to disease state (COPD or control). We further filtered these genes for biological effect using results from a Tukey test post-hoc analysis (Benjamini-Hochberg-adjusted p-value <0.05 and 10% two-tailed quantiles of mean differences between COPD and control), to identify 304 genes. Through analysis of disease, sex, age, and also smoking status and disease interactions we identified differentially expressed genes involved in a variety of immune responses and cell processes in COPD. We also trained a logistic regression model using the 304 genes as features, which enabled prediction of disease status with 84% accuracy. Our results give potential for improving the diagnosis of COPD through blood and highlight novel gene expression disease signatures.

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Meta-analysis based gene expressional profiling reveals functional genes in ovarian cancer

10.21203/rs.3.rs-16156/v1 ◽

2020 ◽

Author(s):

Lin Zhao ◽

Yuhui Li ◽

Zhen Zhang ◽

Hong Guo ◽

Jianfu Li ◽

...

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Differentially Expressed Genes ◽

Molecular Mechanisms ◽

Meta Analysis ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Functional Genes ◽

P Value ◽

Clinical Patient

Abstract Background Ovarian cancer causes high mortality rate worldwide, and despite numerous attempts, the outcome for patients with ovarian cancer are still not well improved. Microarray based gene expressional analysis provides with valuable information for discriminating functional genes in ovarian cancer development and progression. However, due to the differences in experimental design, the results varied significantly across individual datasets. Methods In the present study, the data of gene expression in ovarian cancer was downloaded from Gene Expression Omnibus and 16 studies were included. A meta-analysis based gene expression analysis was performed to identify differentially expressed genes (DEGs). The most differentially expressed genes in our meta-analysis were selected for gene expression and gene function validation. Results A total of 972 DEGs with P -value<0.001 were identified in ovarian cancer, including 541 up-regulated genes and 431 down-regulated genes, among which 92 additional DEGs were found as gained DEGs. Top five up-and down-regulated genes were selected for the validation of gene expressional profiling. Among these genes, up-regulated CD24 , SOX17 , WFDC2 , EPCAM , INAVA , and down-regulated AOX1 were revealed to be with consistent expressional patterns in clinical patient samples of ovarian cancer. Gene functional analysis demonstrated that up-regulated WFCD2 and INAVA promoted ovarian cancer cell migration, WFDC2 enhanced cell proliferation, while down-regulated AOX1 was functional in inducing cell apoptosis of ovarian cancer. Interestingly. Conclusion Our study shed light on the molecular mechanisms underlying the development of ovarian cancer, and facilitated the understanding of novel diagnostic and therapeutic targets in ovarian cancer.

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Transcriptional survey of alveolar macrophages in a murine model of chronic granulomatous inflammation reveals common themes with human sarcoidosis

AJP Lung Cellular and Molecular Physiology ◽

10.1152/ajplung.00289.2017 ◽

2018 ◽

Vol 314 (4) ◽

pp. L617-L625 ◽

Cited By ~ 8

Author(s):

Arjun Mohan ◽

Anagha Malur ◽

Matthew McPeek ◽

Barbara P. Barna ◽

Lynn M. Schnapp ◽

...

Keyword(s):

Differentially Expressed Genes ◽

Murine Model ◽

Alveolar Macrophages ◽

Gene Set Enrichment Analysis ◽

Differentially Expressed ◽

Multiwall Carbon Nanotube ◽

Gene Set Enrichment ◽

Integrated Network ◽

Gene Set ◽

Gene Sets

To advance our understanding of the pathobiology of sarcoidosis, we developed a multiwall carbon nanotube (MWCNT)-based murine model that shows marked histological and inflammatory signal similarities to this disease. In this study, we compared the alveolar macrophage transcriptional signatures of our animal model with human sarcoidosis to identify overlapping molecular programs. Whole genome microarrays were used to assess gene expression of alveolar macrophages in six MWCNT-exposed and six control animals. The results were compared with the transcriptional profiles of alveolar immune cells in 15 sarcoidosis patients and 12 healthy humans. Rigorous statistical methods were used to identify differentially expressed genes. To better elucidate activated pathways, integrated network and gene set enrichment analysis (GSEA) was performed. We identified over 1,000 differentially expressed between control and MWCNT mice. Gene ontology functional analysis showed overrepresentation of processes primarily involved in immunity and inflammation in MCWNT mice. Applying GSEA to both mouse and human samples revealed upregulation of 92 gene sets in MWCNT mice and 142 gene sets in sarcoidosis patients. Commonly activated pathways in both MWCNT mice and sarcoidosis included adaptive immunity, T-cell signaling, IL-12/IL-17 signaling, and oxidative phosphorylation. Differences in gene set enrichment between MWCNT mice and sarcoidosis patients were also observed. We applied network analysis to differentially expressed genes common between the MWCNT model and sarcoidosis to identify key drivers of disease. In conclusion, an integrated network and transcriptomics approach revealed substantial functional similarities between a murine model and human sarcoidosis particularly with respect to activation of immune-specific pathways.

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